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#ifndef _FF_H_
#define _FF_H_
#include <vector>
#include "fdict.h"
#include "hg.h"
class SentenceMetadata;
class FeatureFunction; // see definition below
// if you want to develop a new feature, inherit from this class and
// override TraversalFeaturesImpl(...). If it's a feature that returns /
// depends on context, you may also need to implement
// FinalTraversalFeatures(...)
class FeatureFunction {
public:
std::string name; // set by FF factory using usage()
FeatureFunction() : state_size_() {}
explicit FeatureFunction(int state_size) : state_size_(state_size) {}
virtual ~FeatureFunction();
// override this. not virtual because we want to expose this to factory template for help before creating a FF
static std::string usage(bool show_params,bool show_details) {
return usage_helper("FIXME_feature_needs_name","[no parameters]","[no documentation yet]",show_params,show_details);
}
typedef std::vector<WordID> Features; // set of features ids
protected:
static std::string usage_helper(std::string const& name,std::string const& params,std::string const& details,bool show_params,bool show_details);
static Features single_feature(WordID feat);
public:
// stateless feature that doesn't depend on source span: override and return true. then your feature can be precomputed over rules.
virtual bool rule_feature() const { return false; }
//OVERRIDE THIS:
virtual Features features() const { return Features(); }
// returns the number of bytes of context that this feature function will
// (maximally) use. By default, 0 ("stateless" models in Hiero/Joshua).
// NOTE: this value is fixed for the instance of your class, you cannot
// use different amounts of memory for different nodes in the forest.
inline int NumBytesContext() const { return state_size_; }
// Compute the feature values and (if this applies) the estimates of the
// feature values when this edge is used incorporated into a larger context
inline void TraversalFeatures(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* out_state) const {
TraversalFeaturesImpl(smeta, edge, ant_contexts,
features, estimated_features, out_state);
// TODO it's easy for careless feature function developers to overwrite
// the end of their state and clobber someone else's memory. These bugs
// will be horrendously painful to track down. There should be some
// optional strict mode that's enforced here that adds some kind of
// barrier between the blocks reserved for the residual contexts
}
// if there's some state left when you transition to the goal state, score
// it here. For example, the language model computes the cost of adding
// <s> and </s>.
virtual void FinalTraversalFeatures(const void* residual_state,
SparseVector<double>* final_features) const;
protected:
// context is a pointer to a buffer of size NumBytesContext() that the
// feature function can write its state to. It's up to the feature function
// to determine how much space it needs and to determine how to encode its
// residual contextual information since it is OPAQUE to all clients outside
// of the particular FeatureFunction class. There is one exception:
// equality of the contents (i.e., memcmp) is required to determine whether
// two states can be combined.
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const = 0;
// !!! ONLY call this from subclass *CONSTRUCTORS* !!!
void SetStateSize(size_t state_size) {
state_size_ = state_size;
}
private:
int state_size_;
};
// word penalty feature, for each word on the E side of a rule,
// add value_
class WordPenalty : public FeatureFunction {
public:
Features features() const;
WordPenalty(const std::string& param);
static std::string usage(bool p,bool d) {
return usage_helper("WordPenalty","","number of target words (local feature)",p,d);
}
bool rule_feature() const { return true; }
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const;
private:
const int fid_;
const double value_;
};
class SourceWordPenalty : public FeatureFunction {
public:
bool rule_feature() const { return true; }
Features features() const;
SourceWordPenalty(const std::string& param);
static std::string usage(bool p,bool d) {
return usage_helper("SourceWordPenalty","","number of source words (local feature, and meaningless except when input has non-constant number of source words, e.g. segmentation/morphology/speech recognition lattice)",p,d);
}
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const;
private:
const int fid_;
const double value_;
};
#define DEFAULT_MAX_ARITY 9
#define DEFAULT_MAX_ARITY_STRINGIZE(x) #x
#define DEFAULT_MAX_ARITY_STRINGIZE_EVAL(x) DEFAULT_MAX_ARITY_STRINGIZE(x)
#define DEFAULT_MAX_ARITY_STR DEFAULT_MAX_ARITY_STRINGIZE_EVAL(DEFAULT_MAX_ARITY)
class ArityPenalty : public FeatureFunction {
public:
bool rule_feature() const { return true; }
Features features() const;
ArityPenalty(const std::string& param);
static std::string usage(bool p,bool d) {
return usage_helper("ArityPenalty","[MaxArity(default " DEFAULT_MAX_ARITY_STR ")]","Indicator feature Arity_N=1 for rule of arity N (local feature). 0<=N<=MaxArity(default " DEFAULT_MAX_ARITY_STR ")",p,d);
}
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const;
private:
std::vector<WordID> fids_;
const double value_;
};
// this class is a set of FeatureFunctions that can be used to score, rescore,
// etc. a (translation?) forest
class ModelSet {
public:
ModelSet() : state_size_(0) {}
ModelSet(const std::vector<double>& weights,
const std::vector<const FeatureFunction*>& models);
// sets edge->feature_values_ and edge->edge_prob_
// NOTE: edge must not necessarily be in hg.edges_ but its TAIL nodes
// must be.
void AddFeaturesToEdge(const SentenceMetadata& smeta,
const Hypergraph& hg,
const std::vector<std::string>& node_states,
Hypergraph::Edge* edge,
std::string* residual_context,
prob_t* combination_cost_estimate = NULL) const;
void AddFinalFeatures(const std::string& residual_context,
Hypergraph::Edge* edge) const;
bool empty() const { return models_.empty(); }
bool stateless() const { return !state_size_; }
FeatureFunction::Features all_features(std::ostream *warnings=0,bool warn_fid_zero=false); // this will warn about duplicate features as well (one function overwrites the feature of another). also resizes weights_ so it is large enough to hold the (0) weight for the largest reported feature id. since 0 is a NULL feature id, it's never included. if warn_fid_zero, then even the first 0 id is
void show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt=true); //show features and weights
private:
std::vector<const FeatureFunction*> models_;
std::vector<double> weights_;
int state_size_;
std::vector<int> model_state_pos_;
};
#endif
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